faker vs chance vs casual-browserify
JavaScript Data Generation Libraries Comparison
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What's JavaScript Data Generation Libraries?

Data generation libraries are essential tools in web development for creating mock data for testing, prototyping, or populating databases. These libraries provide developers with the ability to generate random data such as names, addresses, dates, and more, which can be invaluable for testing applications without relying on real user data. They help simulate various scenarios and ensure that applications can handle a wide range of inputs, enhancing robustness and reliability.

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faker2,176,653-10.1 MB--MIT
chance1,524,3856,5272.13 MB17810 days agoMIT
casual-browserify11,73316347 kB0-MIT
Feature Comparison: faker vs chance vs casual-browserify

Data Variety

  • faker:

    Faker offers an extensive range of realistic fake data types, including user profiles, addresses, company names, and product details. This makes it ideal for applications that need detailed and varied datasets.

  • chance:

    Chance excels in data variety, offering a wide array of data types including names, addresses, dates, and even custom data types. This flexibility allows developers to generate highly specific datasets tailored to their needs.

  • casual-browserify:

    Casual-Browserify provides a limited set of data types, focusing on basic random data generation such as names, addresses, and dates. It is suitable for simple applications where extensive data variety is not a requirement.

Ease of Use

  • faker:

    Faker has a slightly steeper learning curve due to its comprehensive features and options. However, once familiarized, developers can leverage its powerful capabilities to generate complex datasets efficiently.

  • chance:

    Chance has a user-friendly API that balances simplicity with flexibility. While it offers a variety of options, it remains easy to use for developers who want to generate random data without extensive setup.

  • casual-browserify:

    Casual-Browserify is designed for simplicity and ease of use, making it accessible for developers who need to generate random data quickly without a steep learning curve. Its straightforward API allows for rapid implementation.

Customization

  • faker:

    Faker provides extensive customization options, allowing developers to define formats and structures for the generated data. This is particularly useful for applications requiring specific data layouts.

  • chance:

    Chance allows for significant customization, enabling developers to create custom data types and specify parameters for data generation, making it highly adaptable to various use cases.

  • casual-browserify:

    Casual-Browserify offers limited customization options, primarily focused on generating standard random data without much control over the output format or structure.

Performance

  • faker:

    Faker is designed to generate large volumes of realistic data quickly, making it suitable for applications that require extensive datasets for testing or development.

  • chance:

    Chance is optimized for performance and can handle generating larger datasets efficiently while maintaining randomness and variety in the output.

  • casual-browserify:

    Casual-Browserify is lightweight and performs well for small-scale data generation tasks. However, it may not be optimized for generating large datasets quickly.

Community and Support

  • faker:

    Faker boasts a large community and extensive documentation, offering numerous resources, examples, and support options, making it easier for developers to find help and share knowledge.

  • chance:

    Chance has a moderate community and some resources available, providing a decent level of support for developers looking for help or examples.

  • casual-browserify:

    Casual-Browserify has a smaller community and fewer resources available for support, which may limit assistance for troubleshooting or advanced use cases.

How to Choose: faker vs chance vs casual-browserify
  • faker:

    Opt for Faker if you are looking for a comprehensive library that can generate realistic fake data for a variety of use cases, including user profiles, addresses, and company information. It is particularly beneficial for applications that require detailed and structured data.

  • chance:

    Select Chance if you require a more versatile and feature-rich library that offers a wide variety of data generation options, including custom data types and a strong focus on randomness. It is ideal for applications needing diverse datasets and more control over the generated data.

  • casual-browserify:

    Choose Casual-Browserify if you need a lightweight and straightforward solution for generating random data in a browser environment. It is particularly useful for quick prototyping and simple applications where ease of use is a priority.

Similar Npm Packages to faker

faker is a popular library for generating fake data in JavaScript applications. It is widely used for testing, prototyping, and populating databases with realistic-looking data. Faker provides a variety of methods to create names, addresses, phone numbers, emails, and much more, making it an essential tool for developers who need to create sample data quickly and easily. While faker is a robust solution for generating fake data, there are several alternatives worth considering:

  • casual is a lightweight library that offers a simple API for generating fake data. It provides a wide range of data types, including names, addresses, dates, and even custom data generators. Casual is particularly useful for developers looking for a straightforward and easy-to-use library without the overhead of more complex solutions. Its flexibility allows for the creation of custom generators, making it a versatile choice for various data generation needs.
  • chance is another powerful library for generating random data. It offers a rich set of features, including the ability to generate random numbers, strings, booleans, and even entire objects. Chance is designed to be both flexible and easy to use, making it suitable for a variety of applications, from testing to game development. With its extensive documentation and active community, chance is a reliable alternative for developers who need to generate random data quickly and efficiently.
  • randexp is a specialized library focused on generating random strings based on regular expressions. It allows developers to create complex patterns and generate random data that adheres to those patterns. Randexp is particularly useful when you need to generate data that follows specific formats, such as email addresses or phone numbers. If your data generation needs require strict adherence to patterns, randexp is an excellent choice.

To see how faker compares with casual, chance, and randexp, check out the comparison: Comparing casual vs chance vs faker vs randexp.

Similar Npm Packages to chance

chance is a popular JavaScript library for generating random data. It is particularly useful for developers who need to create mock data for testing, development, or demonstration purposes. With a wide array of methods, chance can generate everything from random names and addresses to dates, numbers, and even custom data structures. Its flexibility and ease of use make it a go-to choice for many developers looking to populate their applications with realistic data.

However, there are several alternatives to chance that also provide similar functionality:

  • casual is another library for generating fake data in JavaScript. It offers a straightforward API and a variety of data types, including names, addresses, and even user profiles. One of the key features of casual is its ability to create custom generators, allowing developers to tailor the data generation process to their specific needs. If you're looking for a simple and customizable solution for generating random data, casual is a great option.

  • faker is one of the most widely used libraries for generating fake data. It provides a rich set of methods for creating realistic data, including names, addresses, phone numbers, and much more. faker is highly versatile and can be used in various environments, including Node.js and the browser. Its extensive documentation and active community make it a reliable choice for developers who need to generate large amounts of mock data quickly.

  • mockjs is a library that not only generates random data but also allows you to create mock APIs. It is particularly useful for front-end developers who want to simulate server responses without needing a real backend. mockjs can generate complex data structures and supports template syntax for defining the shape of the data. If you need to mock API responses along with generating random data, mockjs is a powerful tool to consider.

  • random-words is a lightweight library focused specifically on generating random words. It is ideal for scenarios where you need to create placeholder text or random strings without the complexity of generating full data objects. If your requirements are simple and you only need random words, random-words offers a straightforward solution.

To see how chance compares with casual, faker, mockjs, and random-words, check out the comparison: Comparing casual vs chance vs faker vs mockjs vs random-words.

Similar Npm Packages to casual-browserify

casual-browserify is a lightweight library that provides a simple way to generate random data for use in applications. It is particularly useful for testing and development purposes, allowing developers to create realistic-looking data without the need for a complex setup. With casual-browserify, you can easily generate names, addresses, phone numbers, and other types of data, making it a handy tool for populating databases or creating mock APIs.

While casual-browserify is a great option for generating random data, there are other libraries in the ecosystem that offer similar functionalities. Here are a couple of alternatives:

  • chance is a popular library for generating random data in JavaScript. It provides a wide range of data types, including names, addresses, dates, and even custom data types. Chance is highly configurable, allowing developers to specify the format and constraints of the generated data. If you're looking for a robust and flexible solution for generating random data, Chance is an excellent choice that has been widely adopted in the community.

  • faker is another well-known library for generating fake data. It offers a rich set of features for creating realistic data for various use cases, including user profiles, product information, and more. Faker is particularly useful for generating large datasets quickly and easily. If your project requires extensive fake data generation, Faker provides a comprehensive set of tools to meet those needs.

To see how casual-browserify compares with Chance and Faker, check out the comparison: Comparing casual-browserify vs chance vs faker.

README for faker

faker.js - generate massive amounts of fake data in the browser and node.js

Faker.js

Build Status Coverage Status

npm version

OpenCollective OpenCollective Gitter chat

Demo

https://rawgit.com/Marak/faker.js/master/examples/browser/index.html

Faker Cloud

Don't have a local development setup ready?

Try our hosted version of Faker at https://fakercloud.com/api

https://github.com/faker/faker-cloud

Usage

Browser

    <script src = "faker.js" type = "text/javascript"></script>
    <script>
      var randomName = faker.name.findName(); // Caitlyn Kerluke
      var randomEmail = faker.internet.email(); // Rusty@arne.info
      var randomCard = faker.helpers.createCard(); // random contact card containing many properties
    </script>

Node.js

    var faker = require('faker');

    var randomName = faker.name.findName(); // Rowan Nikolaus
    var randomEmail = faker.internet.email(); // Kassandra.Haley@erich.biz
    var randomCard = faker.helpers.createCard(); // random contact card containing many properties

API

JSDoc API Browser

http://marak.github.io/faker.js/

API Methods

  • address
    • zipCode
    • zipCodeByState
    • city
    • cityPrefix
    • citySuffix
    • cityName
    • streetName
    • streetAddress
    • streetSuffix
    • streetPrefix
    • secondaryAddress
    • county
    • country
    • countryCode
    • state
    • stateAbbr
    • latitude
    • longitude
    • direction
    • cardinalDirection
    • ordinalDirection
    • nearbyGPSCoordinate
    • timeZone
  • animal
    • dog
    • cat
    • snake
    • bear
    • lion
    • cetacean
    • horse
    • bird
    • cow
    • fish
    • crocodilia
    • insect
    • rabbit
    • type
  • commerce
    • color
    • department
    • productName
    • price
    • productAdjective
    • productMaterial
    • product
    • productDescription
  • company
    • suffixes
    • companyName
    • companySuffix
    • catchPhrase
    • bs
    • catchPhraseAdjective
    • catchPhraseDescriptor
    • catchPhraseNoun
    • bsAdjective
    • bsBuzz
    • bsNoun
  • database
    • column
    • type
    • collation
    • engine
  • datatype
    • number
    • float
    • datetime
    • string
    • uuid
    • boolean
    • hexaDecimal
    • json
    • array
  • date
    • past
    • future
    • between
    • betweens
    • recent
    • soon
    • month
    • weekday
  • fake
  • finance
    • account
    • accountName
    • routingNumber
    • mask
    • amount
    • transactionType
    • currencyCode
    • currencyName
    • currencySymbol
    • bitcoinAddress
    • litecoinAddress
    • creditCardNumber
    • creditCardCVV
    • ethereumAddress
    • iban
    • bic
    • transactionDescription
  • git
    • branch
    • commitEntry
    • commitMessage
    • commitSha
    • shortSha
  • hacker
    • abbreviation
    • adjective
    • noun
    • verb
    • ingverb
    • phrase
  • helpers
    • randomize
    • slugify
    • replaceSymbolWithNumber
    • replaceSymbols
    • replaceCreditCardSymbols
    • repeatString
    • regexpStyleStringParse
    • shuffle
    • mustache
    • createCard
    • contextualCard
    • userCard
    • createTransaction
  • image
    • image
    • avatar
    • imageUrl
    • abstract
    • animals
    • business
    • cats
    • city
    • food
    • nightlife
    • fashion
    • people
    • nature
    • sports
    • technics
    • transport
    • dataUri
    • lorempixel
    • unsplash
    • lorempicsum
  • internet
    • avatar
    • email
    • exampleEmail
    • userName
    • protocol
    • httpMethod
    • url
    • domainName
    • domainSuffix
    • domainWord
    • ip
    • ipv6
    • port
    • userAgent
    • color
    • mac
    • password
  • lorem
    • word
    • words
    • sentence
    • slug
    • sentences
    • paragraph
    • paragraphs
    • text
    • lines
  • mersenne
    • rand
    • seed
    • seed_array
  • music
    • genre
  • name
    • firstName
    • lastName
    • middleName
    • findName
    • jobTitle
    • gender
    • prefix
    • suffix
    • title
    • jobDescriptor
    • jobArea
    • jobType
  • phone
    • phoneNumber
    • phoneNumberFormat
    • phoneFormats
  • random
    • number
    • float
    • arrayElement
    • arrayElements
    • objectElement
    • uuid
    • boolean
    • word
    • words
    • image
    • locale
    • alpha
    • alphaNumeric
    • hexaDecimal
  • system
    • fileName
    • commonFileName
    • mimeType
    • commonFileType
    • commonFileExt
    • fileType
    • fileExt
    • directoryPath
    • filePath
    • semver
  • time
    • recent
  • unique
  • vehicle
    • vehicle
    • manufacturer
    • model
    • type
    • fuel
    • vin
    • color
    • vrm
    • bicycle

Faker.fake()

faker.js contains a super useful generator method Faker.fake for combining faker API methods using a mustache string format.

Example:

console.log(faker.fake("{{name.lastName}}, {{name.firstName}} {{name.suffix}}"));
// outputs: "Marks, Dean Sr."

This will interpolate the format string with the value of methods name.lastName(), name.firstName(), and name.suffix()

Localization

As of version v2.0.0 faker.js has support for multiple localities.

The default language locale is set to English.

Setting a new locale is simple:

// sets locale to de
faker.locale = "de";
  • az
  • ar
  • cz
  • de
  • de_AT
  • de_CH
  • en
  • en_AU
  • en_AU_ocker
  • en_BORK
  • en_CA
  • en_GB
  • en_IE
  • en_IND
  • en_US
  • en_ZA
  • es
  • es_MX
  • fa
  • fi
  • fr
  • fr_CA
  • fr_CH
  • ge
  • hy
  • hr
  • id_ID
  • it
  • ja
  • ko
  • nb_NO
  • ne
  • nl
  • nl_BE
  • pl
  • pt_BR
  • pt_PT
  • ro
  • ru
  • sk
  • sv
  • tr
  • uk
  • vi
  • zh_CN
  • zh_TW

Individual Localization Packages

faker.js supports incremental loading of locales.

By default, requiring faker will include all locale data.

In a production environment, you may only want to include the locale data for a specific set of locales.

// loads only de locale
var faker = require('faker/locale/de');

Setting a randomness seed

If you want consistent results, you can set your own seed:

faker.seed(123);

var firstRandom = faker.datatype.number();

// Setting the seed again resets the sequence.
faker.seed(123);

var secondRandom = faker.datatype.number();

console.log(firstRandom === secondRandom);

Tests

npm install .
make test

You can view a code coverage report generated in coverage/lcov-report/index.html.

Building faker.js

faker uses gulp to automate its build process. Each build operation is a separate task which can be run independently.

Browser Bundle

npm run browser

Building JSDocs

JSDOC v3 HTML API documentation

npm run jsdoc

Building ReadMe

The ReadMe.md file for faker.js is automatically generated and should not be modified directly. All updates to ReadMe.md should be performed in ./build/src/docs.md and then the build script should be run.

npm run readme

Version Release Schedule

faker.js is a popular project used by many organizations and individuals in production settings. Major and Minor version releases are generally on a monthly schedule. Bugs fixes are addressed by severity and fixed as soon as possible.

If you require the absolute latest version of faker.js the master branch @ http://github.com/marak/faker.js/ should always be up to date and working.

Maintainer

Marak Squires

faker.js - Copyright (c) 2020 Marak Squires www.marak.com http://github.com/marak/faker.js/

faker.js was inspired by and has used data definitions from:

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Support us with a monthly donation and help us continue our activities. [Become a backer]

Sponsors

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